Functional brain networks in the evaluation of patients with neurodegenerative disorders.

Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient's scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations. Advances in neuroimaging research have enabled the development of predictive models that integrate information from multiple brain systems. Here, Perovnik, Rus and colleagues discuss the detection and validation of neurodegenerative disease-specific functional brain networks and consider their relationship to pathological processes and disease-related genotypes. Key points: Parkinson disease, Alzheimer disease and other neurodegenerative disorders are characterized by specific disease-related functional topographies (brain networks) that can be identified and validated using metabolic PET or resting-state functional MRI. Brain network activity can be quantified on an individual patient basis, and the resulting network expression levels can be used in research and clinical settings. Expression levels for multiple disease-related topographies can be entered into computational algorithms used to classify patients according to the diagnostic likelihood of these diseases. Expression levels for abnormal disease networks correlate with clinical symptom severity and can be modulated by effective treatment. Network expression levels increase over time and can be used to predict the likelihood of transition from preclinical to symptomatic disease in at-risk individuals. The characterization of treatment-induced networks opens the door to their future use as objective outcome measures in clinical trials. [ABSTRACT FROM AUTHOR]